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Standard supporting 8K VESA Embedded DisplayPort 1.4a

12.02.2015

The Video Electronics Standards Association (VESA) has announced the release of the new Embedded DisplayPort (eDP) version 1.4a specification. The new version of the standard provides for an increase in the bandwidth of the interface for transmitting ultra-high-definition video with greater color depth and a high screen refresh rate. In addition, eDP 1.4a now also includes the VESA Display Stream Compression (DSC) v1.1 data compression standard.

The new specification provides data transfer rates up to 8,1 Gbps per line. Thanks to this, as well as support for VESA DSC 1.1, it is possible to create embedded panels with 8K resolution. DSC compression reduces the size of the buffer for storing frames, reduces power consumption, and also simplifies the system itself.

Among the innovations of eDP 1.4a is the Multi-SST Operation (MSO) function, which supports the architecture of the so-called segmented panel displays (in VESA terminology). MSO allows up to four independent panels to be combined into one system via an eDP interface. Also included in eDP 1.4a is an updated Panel Self Refresh (PSR) feature. The partial update feature allows the video processor to update only a portion of the content on the display, only that which has changed. This increases energy saving.

Systems based on the new specification will be on the market by 2016.

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What does the neural network see? 05.03.2019

Open AI and Google have created a new tool called Activation Atlases to visualize interactions between neurons in AI systems. It allows you to penetrate into the "consciousness" of the machine in order to better understand the internal decision-making processes of artificial intelligence, as well as to identify their errors and shortcomings. Activation Atlases is built around the capability visualization method. It provides an insight into what the hidden layers of neural networks are, which in turn make machine learning more accessible and interpretable. In other words, it allows researchers to analyze how individual algorithms work, understanding how they combine various abstract elements to identify specific objects.

Activation Atlases will finally help answer the question of what the neural network actually sees. Similar studies have been done before, but then it was like identifying individual letters in the visual alphabet of algorithms. The new study is closer to the whole dictionary, it explains how these letters are combined into words. One of the authors of the work compares Activation Atlases with a microscope for neural networks.

Open AI believes that this technology can detect unforeseen problems in neural networks, for example, in places where the network relies on spurious correlations in image classification (where algorithms could confuse dark-skinned people with gorillas), or when reusing a function leads to strange mistakes.

Activation Atlases uses a well-known neural network called GoogLeNet or InceptionV1, which is trained on images from the ImageNet database. It processes data through about ten layers of hundreds of neurons. In turn, each neuron fires to a different extent on different types of images. The researchers traced this process and found out exactly which elements of the images evoke a response from the system.

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